Note that feature selection is a research topic highly active in the Machine Learning community, especially on non-physics-related topics and classification tasks, with plenty of articles dedicated to the question (to name a few, [1]}, [2]}, [3]}, [4]}, [5]}, [6]}).
Researchers have gathered existing methods into five main categories: filter methods, wrapper methods, embedded methods, ensemble methods, and integrative methods (see, for instance, [7]}, [6]}, [9]} for details). For this work, we are particularly interested in finding methods to identify a priori (before any training) some relevant features just by analyzing existing data. The techniques developed in this article can be viewed as assistive tools for classical or data-driven modeling. It falls into the filter methods category, where numerous approaches have been considered based on linear correlation, Fisher score, etc. (see, for instance, [7]}).
